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# Quick Start

This chapter will get you started with ByteMLPerf using a simple executable example.

## Usage

The user uses launch.py as the entry point. When using byte mlperf to evaluate the model, you only need to pass in two parameters ``--task`` and ``--hardware_type``, as shown below:
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```sh
python3 launch.py --task xxx --hardware_type xxx
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```sh
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1. **``--task``**: parameter is the name of the incoming workload. You need to specify the workload. For example, if you would like to evaluate the workload: ``bert-tf-fp16.json``, you need to specify ``--task bert-tf-fp16``.
Note: All workloads are defined under ``byte_mlperf/workloads``, and the name needs to be aligned with the file name when passing parameters. The current format is model-framework-precision.

2. **``--hardware_type``**: parameter is the incoming hardware_type name, there is no default value, it must be specified by the user. Example: To evaluate Habana Goya, specify --hardware_type GOYA .
Note: All hardware types are defined under byte_mlperf/backends, and the name needs to be aligned with the folder name when passing parameters.

3. **``--compile_only``**: parameter will make task stoped once compilation is finished

4. **``--show_task_list``**: parameter will print all task name

5. **``--show_hardware_list``**: parameter will print all hardware backend
